Extending audio tracks while avoiding audio discontinuities

ABSTRACT

Embodiments disclosed herein extending an audio track by joining similar portions. Audio features (e.g., spectral features, modulation features) may be extracted from the audio track. The audio track may be segmented, e.g., based on the audio features, and each segment may be slid through the audio track using a timestep. In each timestep, the sliding segment may be compared to the underlying portion of the audio track and a similarity score (e.g., a cross-correlation) may be generated. A self-similarity matrix may be generated based on the comparisons involving all the segments. The self-similarity matrix may be analyzed for peak values and segments corresponding to the peak values may be joined to extend the audio track. The embodiments may be applied to any kind of audio including music, ambient noise, speech, etc.

RELATED APPLICATIONS

This application is related to U.S. Pat. Nos. 7,674,224; 10,653,857;U.S. Patent Publication No. 2020/0265827; and U.S. patent applicationSer. Nos. 17/366,896, 17/505,453; all of which are incorporated hereinby reference in their entirety.

BACKGROUND

For decades, neuroscientists have observed wave-like activity in thebrain called neural oscillations. Aspects of these neural oscillationshave been found to be related to mental states including attention,relaxation, and sleep. The ability to effectively induce and modify suchmental states by noninvasive brain stimulation is desirable.

BRIEF DESCRIPTION OF DRAWINGS

Features, aspects, and advantages of the present disclosure will becomeapparent to those skilled in the art upon reading the following detaileddescription of exemplary embodiments and appended claims, in conjunctionwith the accompanying drawings, in which like reference numerals havebeen used to designate like elements, and in which:

FIG. 1 depicts a flow diagram of an illustrative method of extending anaudio track, according to some embodiments of the present disclosure.

FIG. 2A depicts a process diagram of an illustrative method ofgenerating a self-similarity matrix, according to some embodiments ofthe present disclosure;

FIG. 2B depicts an illustrative self-similarity matrix, according tosome embodiments of the present disclosure;

FIG. 3A depicts a process diagram of an illustrative method of joiningsegments to extend an audio track, according to some embodiments of thepresent disclosure;

FIG. 3B depicts a process diagram of another illustrative method ofjoining segments to extend an audio track, according to some embodimentsof the present disclosure;

FIG. 3C depicts a process diagram of an illustrative method ofcalculating optimal join (overlap) point for segments to be joined,according to some embodiments of the present disclosure;

FIG. 4 depicts a functional block diagram of an illustrative processingdevice according to some embodiments of the present disclosure; and

FIG. 5 depicts an illustrative system with various components forextending an audio track, according to some embodiments of the presentdisclosure.

The figures are for purposes of illustrating example embodiments, but itis understood that the present disclosure is not limited to thearrangements and instrumentality shown in the drawings. In the figures,identical reference numbers identify at least generally similarelements.

DESCRIPTION

Current audio playback systems are generally based on sequentiallyplaying audio tracks; e.g., playing a first audio track from start tofinish followed by a second audio track, and so forth. This has theeffect of presenting variety to the user which may maintain the user'scontinued interest in and engagement with the audio. However, this maynot be the desired result for audio used to aid focus (e.g., focusing ona task rather than paying attention to the music), sleep, or relaxation.Furthermore, switching from one audio track to the next may introducediscontinuities in audio characteristics such as a brief silence in theaudio and/or a change in the audio modulation, rhythm, instrumentation,and the like. With popular music, such discontinuities may occur every3-5 minutes (the length of a normal music track). This switching betweentracks may be disruptive to the listener attempting to maintain adesired mental state (e.g., being focused). One potential solution maybe to loop (e.g., repeat) a single track, but often this may stillresult in discontinuities because of the different audio characteristicsbetween the “outro” (e.g., final portion) and “intro” (e.g., initialportion) of the audio track. It is therefore desirable to extend anaudio track, creating a version longer than the original track byrepeating audio from the original track by non-perceptible, seamlessjoining of various portions of the audio track such that a listener canmaintain a desired mental state for a desired length of time.

Embodiments disclosed herein describe techniques for extending an audiotrack with non-perceptible, seamless joining of different portions ofthe audio track. The joining may be based on the similarity of audiocharacteristics within the audio track, such as similarity betweenamplitude modulation characteristics of different portions of the audiotrack. The similarity analysis for amplitude modulation may includedetermining characteristics (e.g., constituent frequencies) of the soundenvelope, rather than the constituent frequencies of the audio itself.The sound envelope, which may move slower than the frequencies of theaudio itself, is known to be a more perceptible feature of sound in themammalian brain. Research shows that mammalian auditory system involvesa modulation-frequency filter bank (e.g., allowing the brain todiscriminate between modulation frequencies of the sound envelope) inthe brain stem and audio-frequency filter bank (e.g., allowing the brainto discriminate between frequencies in the audio signal itself) in thecochlea. Research also shows that amplitude modulation may driverhythmic activity in the brain, which may then be leveraged to supportmental states like focus, sleep, relaxation, and/or various other mentalstates.

The modulation-frequency domain may generally include 0.1 Hz-100 Hz(compared to audible frequency range of 20 Hz-20 KHz). Modulationfrequencies (or modulation rates) may refer to the spectra of amplitudechanges in an underlying higher-frequency signal (the audio-frequency“carrier”). Extraction of the modulation characteristics may include,e.g., determining the envelope of a sound (broadband or filteredsub-bands) via a technique like Hilbert transform; followed by aspectral analysis of this envelope via methods like Fast FourierTransforms (FFTs) or modulation domain bandpass filtering (e.g., todetermine the spectrum of the sound envelope), visual filtering on thespectrographic representation of the sound envelope, and/or any othertechnique of extracting modulation characteristics.

The usage of modulation characteristics for audio track extension fordetermining similarity is just an example; and usage of othercharacteristics should also be considered within the scope of thisdisclosure. For example, one or more embodiments may use acousticcharacteristics such as audio-frequency, brightness, complexity, musicalsurprise, etc. that may bear on effectiveness, distractibility, andmodification of mental states, etc. One or more of these characteristicsmay be used to provide an audio output targeted to elicit a desiredmental state, whereby the duration of the audio track can be arbitrarilyadjusted to different time durations without sounding repetitive,without introducing discontinuities, or otherwise losing itseffectiveness of eliciting a desired mental state.

For example, an earlier segment may be joined to a later segment havingsimilar audio characteristics as the earlier segment. Using the joiningbetween the various portions of the audio track, the audio track may beextended. For instance, a five-minute music piece may be extended to anhour of playback. These embodiments of track extension may be applicableto environmental sounds, speech, music with poorly defined beats (e.g.,ambient, metrically-variable music), music with well-defined beats,and/or any other type of audio content.

In an example method of extending an audio track, multi-dimensionalfeatures of the audio track (e.g., amplitude modulation features) may beextracted. The extracted multi-dimensional features may be in the formof a spectrogram, a cochleagram, and/or any other form of audiofeatures. The extracted multi-dimensional features may be used togenerate an “image” representation of the sound. For example, the imagerepresentation may be a 2-dimensional image with the frequency spectrum(e.g., of the sound envelope) on the y-axis and the time on the x-axis.

To determine the similarity between different portions of the audiotrack, the audio track (e.g., the features extracted from the audiotrack) may be divided into a plurality of segments. The size of thesegment may be based on the extracted multi-dimensional features. In thecase of rhythmic sounds such as music, the segment size may comprise acertain number of beats (e.g., four beats; one beat is often assignedthe value of a quarter-note in western popular music); for non-rhythmicsound such as ambient sound, the segment size may be based on a timeduration (e.g., an absolute time duration of 3 seconds).

Each of the segments may then be compared with the entirety of the audiotrack. For example, a timestep smaller than the segment size may bechosen, and a given segment may be slid across the audio track using thetimestep. At each time step, the features of the segment may be comparedto the features of the underlying portion of the audio track associatedwith the current timestep. The comparison may include, for example,cross-correlation, difference, division, and/or any other type ofsimilarity analysis. Therefore, the sliding and comparison operationsfor each segment may generate a similarity vector indicating thesimilarity between the segment and different portions of the audio trackat each timestep.

The sliding and comparison operations may be performed for each of thesegments of the audio track thereby generating a similarity vector foreach segment. The similarity vectors for all the segments may becombined to generate a self-similarity matrix. In an exampleself-similarity matrix, each row may be a similarity vector for adifferent segment and may contain column entries for each time step.Therefore, if there are M number of segments and T number of timesteps,the self-similarity matrix has 2 dimensions with size M*T. An element(X,Y) of the self-similarity matrix may be a numerical value indicatingthe similarity between the corresponding segment X and the correspondingunderlying portion of the audio track at timestep Y.

Similarity between different portions of the audio track may bedetermined based on an analysis of the self-similarity matrix. Forexample, within the self-similarity matrix, the elements may includepeaks (e.g., an element with a higher value than its neighbors) showinga higher similarity between the corresponding portions. The joining foraudio track extension may be for the segments corresponding to thesepeaks. A thresholding may be applied during an analysis of theself-similarity matrix and the segments associated with a predeterminednumber of highest-valued peaks may be identified as candidates forjoining. In addition to similarity (as indicated by the peaks), thejoining may be based on other considerations such as whether thecorresponding segment appears toward the beginning of the audio track ortowards the end of the audio track, whether the corresponding segmentwas used for extension before, and/or any other considerations.

When two segments are selected for joining, a cross-correlation (and/orany other form of similarity analysis) may be performed between theenvelopes of the segments. The cross-correlation may determine anadditional time-shift between the two segments, smaller than the segmentsize, which may be imposed before they are joined.

The optimal point for joining two segments (e.g, via a rapid crossfade)may then be determined by finding a location with relatively low energysuch as, for example, a zero crossing or where the sound envelope has alow value. When the joining point is determined, the correspondingsegments are joined to extend the audio track.

In an embodiment, a computer-implemented method is provided. The methodmay include extracting multi-dimensional features from an audio signal;segmenting the audio signal into a first plurality of segments eachhaving a segment size and extracted multi-dimensional features;segmenting the audio signal into a second plurality of segments eachhaving the segment size and the extracted multi-dimensional features;selecting at least one segment from the first plurality of segments, andfor each selected segment: comparing the multi-dimensional features ofthe segment with the multi-dimensional features of the second pluralityof segments; generating a self-similarity matrix having valuesindicating comparisons of the multi-dimensional features of the selectedsegment with multi-dimensional features of the second plurality ofsegments; selecting a first segment from the first plurality of segmentsand a second segment from the second plurality of segments, wherein thefirst and second segments correspond to a value in the self-similaritymatrix that is greater than a threshold; and joining a first portion ofthe audio signal and a second portion of the audio signal, wherein thefirst portion of the audio signal includes the first segment, andwherein the second portion of the audio signal includes the secondsegment.

In another embodiment, a system is provided. The system may include aprocessor; and a tangible, non-transitory computer readable mediumstoring computer program instructions, that when executed by theprocessor, cause the system to perform operations comprising: extractingmulti-dimensional features from an audio signal; segmenting the audiosignal into a first plurality of segments each having a segment size andextracted multi-dimensional features; segmenting the audio signal into asecond plurality of segments each having the segment size and theextracted multi-dimensional features; selecting at least one segmentfrom the first plurality of segments and for each selected segment:comparing the multi-dimensional features of the segment with themulti-dimensional features of the plurality of segments; generating aself-similarity matrix having values indicating comparisons of themulti-dimensional features of the selected segment withmulti-dimensional features of the second plurality of segments;selecting a first segment from the first plurality of segments and asecond segment from the second plurality of segments, wherein the firstand second segments correspond to a value in the self-similarity matrixthat is greater than a threshold; and joining a first portion of theaudio signal and a second portion of the audio signal, wherein the firstportion of the audio signal includes the first segment, and wherein thesecond portion of the audio signal includes the second segment.

In yet another embodiment, a tangible, non-transitory computer readablemedium is provided. The tangible, non-transitory computer readablemedium may store computer program instructions, that when executed by aprocess, may cause operations including extracting multi-dimensionalfeatures from an audio signal; segmenting the audio signal into a firstplurality of segments each having a segment size and extractedmulti-dimensional features; segmenting the audio signal into a secondplurality of segments each having the segment size and the extractedmulti-dimensional features; selecting at least one segment from thefirst plurality of segments and for each selected segment: comparing themulti-dimensional features of the segment with the multi-dimensionalfeatures of the plurality of segments; generating a self-similaritymatrix having values indicating comparisons of the multi-dimensionalfeatures of the selected segment with multi-dimensional features of thesecond plurality of segments; selecting a first segment from the firstplurality of segments and a second segment from the second plurality ofsegments, wherein the first and second segments correspond to a value inthe self-similarity matrix that is greater than a threshold; and joininga first portion of the audio signal and a second portion of the audiosignal, wherein the first portion of the audio signal includes the firstsegment, and wherein the second portion of the audio signal includes thesecond segment.

FIG. 1 illustrates an example method 100 performed by a processingdevice (e.g., smartphone, computer, smart speaker, etc.), according tosome embodiments of the present disclosure. The method 100 may includeone or more operations, functions, or actions as illustrated in one ormore of blocks 102-120. Although the blocks are illustrated insequential order, these blocks may also be performed in parallel, and/orin a different order than the order disclosed and described herein.Also, the various blocks may be combined into fewer blocks, divided intoadditional blocks, and/or removed based upon a desired implementation.

At block 102, an audio track may be segmented. The segmentation may bebased on one or more temporal aspects of the audio track. In theembodiments where the audio track contains music, the segmentation maybe based on rhythmic or temporal aspects of the music such as beatsand/or tempo. For example, a beat-finder or a tempo-finder may be run onthe audio track to determine the metrical grid of the music (e.g., todetermine how the music is temporally organized, and the rate of notesover time). For example, the determined metrical grid may include thelength (e.g., in milliseconds) of a measure, a quarter-note, ahalf-note, a whole-note, etc. Using the determined metrical grid, thesegment size may be selected as having, for example, 4 or 8 beats (1 or2 measures for 4/4 time signature), which may amount to several secondsof the audio track (e.g., 1-5 seconds). However, in the embodimentswhere the audio track is non-rhythmic (e.g., audio track containing anambient sound), the segmentation may be performed using a time duration(e.g., 1-5 seconds) without necessarily tracking the beats.

The length of the segments (e.g., 1-5 seconds) may be consideredrelatively long in the context of audio applications, however therelatively longer segments may more likely provide a coherent joining.An aspect of the disclosure is to find segments in the audio track thatcan be interchanged without disrupting larger-scale structure in theaudio (e.g., for a given segment, finding segments that are surroundedby a similar context). For music, a longer segment may encompass amusically meaningful amount of time. If the segment is relatively short(e.g., 200 ms) for an audio track containing music, joining segments mayhave acoustic continuity but may be musically disruptive.

In some embodiments, the segments may be non-overlapping, e.g., a secondsegment may begin at the end of the first segment. In other embodiments,the segments may be overlapping, e.g., a portion of the second segmentmay lie within the first segment (e.g., the second segment may beginbefore the first segment ends). The segments may have a same length ormay have different lengths.

As an analogy to joining audio segments for an audio track containingmusic, consider joining text segments of a written passage. If textsegments include only single letters and the joining is between thesingle letter segments, the result may be an incomprehensible, jumbledtext. If the text segments include single words and the joining isbetween single word segments, the result may also be incomprehensible,jumbled text (albeit less bad than the one generated using single lettersegments). However, if the segments include several words or a phrase,the joining between these segments may result in a more comprehensibletext (possibly even syntactically well-formed). An exception to usingthe longer segments may be operating on non-musical audio (e.g., ambientsound such as a café noise), where shorter segments may be used becausea musical continuity or coherence may not necessarily be an issue.

At block 104, the audio track may be analyzed to extractmulti-dimensional features. For example, multi-dimensional features (orrepresentations) such as spectrogram or cochleagram (e.g., indicatingfrequency over time), MFCCs (Mel Frequency Cepstral Coefficients),modulation characteristics (e.g., indicating spectral or temporalmodulation over time), and/or other audio features may be extracted froman audio track. The analysis and extraction may be performed on thebroadband audio signal (e.g., entire signal) or a portion of the audiosignal (e.g., a frequency sub-band of the signal). As an example, theextracted multi-dimensional features may include amplitude modulationfeatures of the audio track. The amplitude modulation features maycorrespond to energy across different modulation frequencies over timein the sound envelope of the audio track. Amplitude modulations in thesound envelope have effects on the human brain and mental states thatdiffer depending on the characteristics of the modulation.

At block 106, a portion of the extracted multi-dimensional features maybe selected for cross-correlation. In some embodiments, the selectedfeatures may include spectrogram or cochleagram, which may indicateenergy in frequency bands over time. In other embodiments, the selectedfeatures may include a portion of the spectrogram, where the portion maybe restricted for a frequency range for a more efficient analysis.Additionally or alternatively, the selected features may includeMel-frequency cepstral coefficients (MFCCs), modulation characteristics,and/or any other type of extracted audio features. The selection offeatures may be based on additional analyses of the audio. For example,if an audio analysis determines that the high frequency region of aspectrogram contains relatively little energy or relatively littleinformation, that region may be discarded during the selection; this maybe desirable in this example to reduce computational cost. The selectedfeatures (or features in general) may be referred to as feature vectors.For instance, each segment may have a corresponding feature vectorcontaining the corresponding features as they change over the durationof the segment.

At block 108, a feature vector of one or more segments may becross-correlated with the feature vector of other segments forming atleast a portion of the audio track. For example, a timestep (generallyshorter than the segment size) may be selected, and a given segment maybe slid through at least a portion of the audio track in the incrementsof the time step. At each time step, the cross-correlation (or any othersimilarity measurement) between the segment and the underlying portionof the audio track that the segment is sliding over is recorded. Thissliding process may yield a cross-correlation function (or any othersimilarity indication) that may indicate which segments in the at leasta portion of the audio track best match the sliding segment. It shouldhowever be understood that cross-correlation is just an example ofcomparing the features of the sliding segment with the features of theunderlying portion of the audio track, and other forms of comparisonshould also be considered within the scope of this disclosure.Alternatives to cross-correlation may include, for example, difference,division, etc.

In some embodiments, the timestep for cross-correlation may be a unitfraction of segment size in samples (where the digital audio file is asequence of samples intended to be played back at a predefined samplerate to generate a pressure waveform). For example, if a segment has Nsamples, the cross-correlation timestep may contain N/2, N/3, N/4, N/5,. . . , etc. samples. The segment size may be chosen so as to allowcross-correlation at particular resolutions, e.g., a smaller segmentsize and corresponding smaller timestep for a higher resolution.Regardless of the segment and timestep sizes, the sliding and comparingoperations for each segment may generate a similarity vector.

At block 110, a self-similarity matrix is generated. The self-similaritymatrix may be based on cross-correlations (and/or any form ofcomparison) performed in block 108 and may contain the similarityvectors generated for the plurality of segments. In other words, withinthe self-similarity matrix, a given row may represent thecross-correlation of the corresponding segment with the segments formingat least a portion of the audio track. Accordingly, the self-similaritymatrix may have a size of M (rows)*T (columns) with M being the numberof segments and T being the number of timesteps in the at least aportion of the audio track (which may be based on the size oftimesteps—the smaller the timestep, the larger the T). Theself-similarity matrix may represent the similarity of the M predefinedsegments to other segments forming at least a portion of the audiotrack. However, as described above, cross-correlation is just but anexample of the comparison, and other forms of comparisons should also beconsidered within the scope of this disclosure. For example, other formsof comparisons such as sliding dot-product, subtraction, and/or divisionshould be considered as alternatives or additions to cross-correlation.

At block 112, peaks in the self-similarity matrix may be identified.Each peak in the self-similarity matrix corresponds to a pair ofsegments that are more likely to be similar to each other than toneighboring segments. Therefore the identified peaks may be used in thesubsequent steps for joining the likely similar segments. Identifyingthe peaks to use in joining may include detecting peaks that are higherthan other peaks by thresholding a larger set of peaks, for example bykeeping the highest peaks (e.g., 5 highest peaks) while dropping a peakwhen a higher one is found, or finding all peaks and keeping only thehighest 5% of peaks. At the end of block 112, a list of the highestpeaks and/or the segment-pairs with the highest peaks from theself-similarity matrix may be generated.

At block 114, a peak may be selected as a cut/join point. The selectionmay be based on factors such as peak height (e.g., which may indicatethe level of similarity between corresponding segment and the underlyingportion of the audio track), location (e.g., the location of thecorresponding segment within the audio track), and/or history of usageof the corresponding segment (e.g., a previously used segment may beavoided for joining to reduce the probability of undesirable repetitionin music). These are just a few example considerations in the peakselection, and other peak selection considerations should also beconsidered within the scope of this disclosure.

At block 116, the segments to be joined may be identified. Theidentified segments may correspond to the peak selected as the cut/joinpoint. Accordingly, the identified segments may include (i) the segmentat the peak itself (e.g., the portion of the track representation thatwas being slid over when the high-valued comparison occurred), and (ii)the predetermined segment corresponding to the row containing the peak(e.g., the segment that was sliding over to create the row in theself-similarity matrix). The identified segments, when joined in thesubsequent steps, may be conceptualized as effectively jumping the audiotrack backward or forward in time. For instance, a first identifiedsegment (of the pair indicated by a selected peak) may be farther alongin time (e.g., closer to the end of the original audio track) than asecond identified segment (e.g., which may be closer than the firstidentified segment to the start of the original audio track). Therefore,when the second identified segment is joined after the first identifiedsegment, the audio track may be extended by effectively jumping theaudio track backward in time. Alternatively, when the first identifiedsegment is joined after the second identified segment, the audio trackmay be extended by effectively jumping forward in time (i.e., skippingthe portion of audio between the second and first audio segments) to asimilar segment.

At block 118, audio envelopes around a join point (i.e., the envelopesof the two segments in the pair) may be cross-correlated. Theirbroadband envelopes may be used, or envelopes of filtered sub-bands(envelopes may be determined by, e.g., Hilbert transform, peakinterpolation, and/or other methods). The cross-correlation may beperformed to determine the timeshift required between the identifiedsegments to minimize any perceived discontinuities in the joined audio.Once a maximum in the envelope cross-correlation is found, the requiredtimeshift in samples is known and implemented prior to the joiningoperation. The identified segments may be quite long (contain a largenumber of audio samples) and therefore a join point may have to beidentified with relatively more precision within the duration of the twosimilar segments being joined. This join point is the region over whichthe switch from one audio track to the other occurs (e.g., via a rapidcrossfade). This region may be rather brief, with the crossfade lasting10-500 ms (e.g., not longer than half a second and generally as short as10 ms) to avoid the perception of overlapped tracks. To determine thejoin point, the system may look for the lowest-energy (e.g., quietest)point within the segment(s) because it may be desirable to make the joinat a point where the audio is quiet rather than loud. Determining aquiet point in the segment(s) to make the join can be done using the sumof the segments (e.g., the overlapped audio from the matching pair ofsegments following the determined timeshift), or using only one segmentalone since the two segments are very similar. The determination of aquiet point can be done, for example, via an envelope of the signal orthe raw signal (waveform).

At block 120, two portions of the audio track associated with theidentified segments may be joined. For example, a first portion of theaudio track may correspond to the audio from the start of the track upto and including a first segment, while a second portion of the audiotrack may correspond to the audio from the second segment to the end ofthe track. The joining process may include overlapping (including anydetermined timeshift) the first and second segments, followed byremoving or reducing the loudness to zero a portion of each segmentbefore or after the join point. As a result, the join segment (e.g., thesegment in the joined audio output that is the combination of theoverlapped pair of segments) may include at least a portion of the firstsegment and the second segment. Different methods may be used forjoining two portions of the audio track. In one embodiment, the twoportions of the audio tracks are crossfaded into one another over ashort period of time (which may be different from the segment size). Inanother embodiment, the audio tracks may be joined at coincidentzero-crossings within the join segment. In both these embodiments, theexact join point (e.g., center of the crossfade) can be shifted to lowerenergy points in time nearby, generally within the original joinsegment.

In some embodiments, the extended audio track may be generateddynamically during a playback of the audio track. For example, a usermay, during the playback of the audio track, provide an instruction on auser interface associated with a processing device (e.g., by visuallystretching the timeline for playback of an audio track, by using a voicecommand to extend the track, etc.), and the extended audio track may bedynamically extended. In other embodiments, the user may provide adesired length of the audio track before the beginning of the playback,and the extended audio track may be generated prior to playback. Inanother embodiment, the user provides no explicit instruction, but thetrack continues to play indefinitely with dynamic extension untilplayback is stopped by the user. In yet another embodiment the track maybe dynamically extended in response to sensor data or other input notexplicitly given by the user. For example, a track may dynamicallyextend until environmental conditions change as assessed by a microphoneor light meter.

The selection of the first and second segments may be based onadditional or alternative considerations. For instance, the excessiverepetition of the segments may be avoided as it may be undesirable torepeat the same segment back to back more than 2 or 3 times. To addressthis concern, in some embodiments the previous usage of segment may beconsidered when selecting the first and second segments (e.g., whenpicking a peak in the self-similarity matrix). For example, peaks thathave previously been used as joins, or in which one of the two segmentsindicated by the peak has been used in a join, may be down-weighted orremoved from consideration when selecting new peaks to use in a join. Insome embodiments, joining a segment to itself may be avoided. Theselection of segments to join (i.e., peak selection) may also be basedon the desired time between joins in the resulting extended track. Forexample, it may be undesirable to have join points occur too frequently,and so peaks that would create a join shortly after the latest join maybe down-weighted in favor of peaks that would allow a longer duration ofthe original track to play before another join occurs.

In some embodiments, the “intro” (e.g., initial portion of the track)and “outro” (e.g., final portion of the track) of an audio track may bedisallowed as sections to be joined. For example, the selection of thefirst and/or second segment may be limited to audio segments that occurafter a time interval (e.g., 1 minute) from the beginning of the audiotrack and/or before a time interval (e.g., 1 minute) from the end of theaudio track.

In some embodiments, some portions of the audio track may be excludedfrom repetition. For instance, a portion of the audio track may bedetermined to be an outlier with markedly different characteristicscompared to the other portions of the audio track. As an example, in acafé ambient sound, a portion may haven audio recording of a breakingglass, which may have to be avoided from repeating in the extended audiotrack. This portion may then be disallowed as a join point and/orconsidered as a less favored portion for repetition. Such preference maybe expressed by, for example, negative-weighing the one or more segmentscorresponding to the portion in the self-similarity matrix. Forinstance, the entries in the self-similarity matrix for thecorresponding segments may be set to all zeros. This is just an exampleof enforcing the preference and other methods should also be consideredwithin the scope of this disclosure.

In some embodiments, the first join segment may be selected such thatthe audio track plays unaltered for a period of time before the firstalteration occurs. In other embodiments, the track extension may bedesigned to preserve a structure of the audio track by limiting thejoining of segments from within portions of the audio track. In someembodiments, all parts of the audio track may be available to be usedfor joining segments, minimizing the likelihood that some portions ofthe audio track may be left out completely.

FIG. 2A depicts a process diagram 200 of comparing (e.g.,cross-correlating) a segment of an audio track with the entirety ofaudio track, according to some embodiments of the disclosure. As shown,an audio track 202 may be depicted as a distribution of energy overtime. The audio track 202 may be analyzed to extract a feature vector204. The feature vector 204 may include, e.g., spectrogram, cochleagram,MFCCs, and/or modulation characteristics. A segment 206 of the featurevector may be selected and slid across the feature vector 204 using atime step. A cross-correlation and/or any other type of similarityfunction may be calculated between the segment 206 and the underlyingportion of the feature vector 204. Based on the sliding, a correlation(and/or similarity) function 208 may be generated that may indicate thesimilarity between the segment 206 and the underlying portion of thefeature vector 204. The function 208 may also be referred to as asimilarity vector.

The feature vector 204 may be divided into multiple segments (segment206 is an example of one such segment), and the cross-correlation(and/or similarity) function 208 may be calculated for each segment. Thecross-correlation (and/or similarity) function 208 from the multiplesegments may then be used to generate a self-similarity matrix. FIG. 2Bshows an example self-similarity matrix 210 with M rows {r1, . . . , rM}and T columns {c1, . . . , cT}. The rows of the self-similarity matrix210 may correspond to a number of segments (M). The columns of theself-similarity matrix 210 may correspond to the number of time steps(T). The self-similarity matrix 210 may therefore indicate thesimilarity relationships between the different portions of the audiotrack. As shown, the brightness of the entry (or a pixel) at matrixlocation (m,t) may correspond to the level of similarity between a givensegment m and the underlying portion of the audio track at timestep t.The leading diagonal of the self-similarity matrix 210 may show thestrongest relationship as the leading diagonal may indicate thesimilarity analysis between a segment and itself. Therefore, the leadingdiagonal may be left out in the subsequent peak analysis.

Peak thresholding may be applied to the self-similarity matrix 210 todetermine which segments may be suited to be joined to extend an audiotrack. The peak thresholding may include iterating through theself-similarity matrix 210 to determine the highest peaks (as indicatedby brighter pixels of the self-similarity matrix 210). For instance,five highest peaks may be determined and segments corresponding to oneof the highest peaks (a peak may be selected based on otherconsiderations such as whether a given segment has been used for joiningbefore and/or the location of the segment within the audio track) may bejoined together to extend the audio track. The self-similarity matrix210 may therefore provide an analytical representation of thesimilarities within the audio track, and such representation may be usedto identify the join points for similar portions to extend the audiotrack while avoiding discontinuities.

FIG. 3A depicts a process diagram of an illustrative method 300 a ofjoining segments to extend an audio track, according to some embodimentsof the disclosure. For example, an audio track 308 may be divided into Msegments S₁, S₂, . . . , S_(x−1), S_(x), S_(x+1), . . . , S_(M) (suchsegmented audio track is shown as 308 a), e.g., by using segmentation ofblock 102 of FIG. 1. The audio track 308 may also be divided into Tsegments S*₁, S*₂, . . . , S*_(y−1), S*y, S*_(y+1), . . . , S*T (suchsegmented audio track shown as 308 b). The second segmentation togenerate the T segments may be based on the number of timesteps (e.g., Ttimesteps as described with reference to FIGS. 2A-2B). For example, asshown, the first segment S*₁ of the segmented audio track 308 b may bethe same as the first segment S₁ of the segmented audio track 308 a.However, the second segment S*₂ of the segmented audio track 308 b maybegin after a timestep (which may be smaller than the segment length ofthe segmented audio track 308 a because T>M) and therefore sooner thanthe second segment S₂ of the segmented audio track 308 a. The secondsegment S*₂ of the segmented audio track 308 b is shown spanning twotimesteps, however, it should be understood that other lengths of thesecond segment S*₂ should be considered within the scope of thisdisclosure. The third segment S*₃ of the segmented audio track 308 b isshown to be the same as the second segment S₂ of the segmented audiotrack 308 a and begins before the second segment S*₂ of the segmentedaudio track 308 b has ended.

Therefore, it should be understood that the comparison granularity forjoin analysis (e.g., after the join segments are identified) is notlimited by the predefined segment size used for generating theself-similarity matrix. The join analysis may leverage the smallertimestep (compared to the predefined segment size) for a more granularcomparison to find an optimal join point for identified join segments.Furthermore, the offsetting and the sizing of the segments in thesegmented audio track 308 b compared to the segments of the segmentedaudio track 308 a is not confined to the above example. For instance,the size of the segments in the segmented audio track 308 b may be thelength of the timestep itself (e.g., T=M), or many times greater than atimestep (e.g., T=M*10).

A first segment 302 (S_(x)) and a second segment 304 (S*_(y)) may havebeen selected for joining based on, for example, the peak analysis froma self-similarity matrix (e.g., self-similarity matrix 210). The method300 a of joining a first portion of the audio signal including audioprior to and including the first segment 302 and a second portion of theaudio signal including audio after and including the second segment 304may involve skipping the segments between the first segment 302 and thesecond segment 304. In other words, segments S_(x+1), . . . , S*_(y−1)in between S_(x) and S*_(y) may be absent from the resulting audio track310. Although the resulting audio track shows segments from thesegmented audio track 308 a upstream of the joined segment 306 andsegments from the segmented audio track 308 b downstream of the joinedsegment 306, this is merely for explanation. Other types of segmentationinformation may be used to show the resulting audio track 310.Furthermore, the segmentation information of either the segmented audiotrack 308 a or the segmented audio track 308 b may not be preserved forthe resulting audio track 310.

It should however be understood that the joining of first portion of theaudio signal including audio prior to and including the first segment302 with the second portion of the audio signal including the audioincluding and after the second segment 304 is merely an example, andother manner of joining should also be considered within the scope ofthis disclosure. Another example joining may be between audio up to andincluding the second segment 304 with the audio after and including thefirst segment 302. Therefore, it should generally be understood that thefirst segment 302 may not necessarily be the end point of the firstportion of the audio signal and that the second segment 304 may notnecessarily be the start point of the second portion of the audiosignal.

For joining, audio envelopes (e.g., taken by Hilbert transform of thewaveform, root-mean-square signal magnitude over time, or other methodsof envelope calculation) between the first segment 302 and the secondsegment 304 may be compared using techniques such as cross-correlation,difference measurement, etc. Portions of the first segment 302 and thesecond segment 304 may overlap to generate a joined segment 306 in theresulting audio track 310.

FIG. 3B depicts a process diagram of another illustrative method 300 bof joining segments to extend an audio track, according to an embodimentof the disclosure. For example, an audio track 318 may be divided into Msegments S₁, S₂, . . . , S_(x−1), S_(x), S_(x+1), . . . , S_(M) (suchsegmented audio track is shown as 318 a), e.g., by using segmentation ofblock 102 of FIG. 1. The audio track 318 may also be divided into Tsegments S*₁, S*₂, . . . , S*_(y−1), S*_(y), S*_(y+1), . . . , S*_(T)(such segmented audio track shown as 318 b). The second segmentation togenerate the T segments may be based on the number of timesteps (e.g., Ttimesteps as described with reference to FIGS. 2A-2B). For example, asshown, the first segment S*₁ of the segmented audio track 318 b may bethe same as the first segment S₁ of the segmented audio track 318 a.However, the second segment S*₂ of the segmented audio track 318 b maybegin after a timestep (which may be smaller than the segment length ofthe segmented audio track 318 a because T>M) and therefore sooner thanthe second segment S₂ of the segmented audio track 318 a. The secondsegment S*₂ of the segmented audio track 318 b is shown spanning twotimesteps, however, it should be understood that other lengths of thesecond segment S*₂ should be considered within the scope of thisdisclosure. The third segment S*₃ of the segmented audio track 318 b isshown to be the same as the second segment S₂ of the segmented audiotrack 318 a and begins before the second segment S*₂ of the segmentedaudio track 318 b has ended.

As described above, it should be understood that the comparisongranularity for join analysis (e.g., after the join segments areidentified) is not limited by the predefined segment size used forgenerating the self-similarity matrix. The join analysis may leveragethe smaller timestep (compared to the predefined segment size) for amore granular comparison to find an optimal join point for identifiedjoin segments. Furthermore, the offsetting and the sizing of thesegments in the segmented audio track 318 b compared to the segments ofthe segmented audio track 318 a is not confined to the above example.For instance, the size of the segments in the segmented audio track 318b may be the length of the timestep itself.

A first segment 312 (S_(x)) and a second segment 314 (S*_(y)) may beselected for joining based on, for example, the peak analysis from aself-similarity matrix (e.g., self-similarity matrix 210). In thisexample, a first portion of the audio signal including audio prior toand including the first segment 312 is joined with a second portion ofthe audio signal including audio after and including the second segment314. The resulting audio track 310 is longer than the original track 308and segments S*_(y+1), . . . , S_(x−1) are repeated after the joinedsegment 316. Although the resulting audio track shows segments from thesegmented audio track 318 a upstream of the joined segment 316 andsegments from the segmented audio track 318 b downstream of the joinedsegment 316, this is merely for explanation. Other types of segmentationinformation may be used to show the resulting audio track 320.Furthermore, the segmentation information of either the segmented audiotrack 318 a or the segmented audio track 318 b may not be preserved forthe resulting audio track 310.

It should however be understood that the joining of first portion of theaudio signal including audio prior to and including the first segment312 with the second portion of the audio signal including the audioincluding and after the second segment 314 is merely an example, andother manner of joining should also be considered within the scope ofthis disclosure. Another example joining may be between audio up to andincluding the second segment 314 with the audio after and including thefirst segment 312. Therefore, it should generally be understood that thefirst segment 312 may not necessarily be the end point of the firstportion of the audio signal and that the second segment 314 may notnecessarily be the start point of the second portion of the audiosignal.

For joining, audio envelopes (e.g., taken by Hilbert transform of thewaveform, root-mean-square signal magnitude over time, or other methodsof envelope calculation) between the first segment 312 and the secondsegment 314 may be compared using techniques such as cross-correlation,difference measurement, etc. Portions of the first segment 312 and thesecond segment 314 may overlap to generate a joined segment 316 in theresulting audio track.

FIG. 3C depicts a process diagram of an illustrative method 300 c ofcalculating an optimal join (overlap) point for segments to be joined,according to some embodiments of the present disclosure. A first portion322 of an audio track is shown having a segment S_(x) that may have tobe joined with segment S*_(y) of a second portion 324 of the audiotrack. To determine the optimal point for joining S_(x) and S*_(y), theaudio signals may be summed (and/or combined in any other way) togenerate a waveform 332. Using the waveform 332, an envelope of thewaveform 334 may be generated. Using the envelope of the waveform 334, alow point 328 may be identified corresponding to a point with arelatively lower energy level when the audio signals from the twosegments S_(x) and S*_(y) are combined. The low point 328 may thereforebe used to identify a join point within the joined segment 326 to createthe extended audio track 330.

FIG. 4 shows a functional block diagram of an illustrative processingdevice 400 that can implement the previously described method 100 andprocesses 200, 300 a, and 300 b. The processing device 400 includes oneor more processors 410, software components 420, memory 430, one or moresensor inputs 440, audio processing components (e.g. audio input) 450, auser interface 460, a network interface 470 including wirelessinterface(s) 472 and/or wired interface(s) 474, and a display 480. Theprocessing device may further include audio amplifier(s) and speaker(s)for audio playback. In one case, the processing device 400 may notinclude the speaker(s), but rather a speaker interface for connectingthe processing device to external speakers. In another case, theprocessing device 400 may include neither the speaker(s) nor the audioamplifier(s), but rather an audio interface for connecting theprocessing device 400 to an external audio amplifier or audio-visualplayback device.

In some examples, the one or more processors 410 include one or moreclock-driven computing components configured to process input dataaccording to instructions stored in the memory 430. The memory 430 maybe a tangible, non-transitory computer-readable medium configured tostore instructions executable by the one or more processors 410. Forinstance, the memory 430 may be data storage that can be loaded with oneor more of the software components 420 executable by the one or moreprocessors 410 to achieve certain functions. In one example, thefunctions may involve the processing device 400 retrieving audio datafrom an audio source or another processing device. In another example,the functions may involve the processing device 400 sending audio datato another device or a playback device on a network.

The audio processing components 450 may include one or moredigital-to-analog converters (DAC), an audio preprocessing component, anaudio enhancement component or a digital signal processor (DSP), and soon. In one embodiment, one or more of the audio processing components450 may be a subcomponent of the one or more processors 410. In oneexample, audio content may be processed and/or intentionally altered bythe audio processing components 450 to produce audio signals. Theproduced audio signals may be further processed and/or provided to anamplifier for playback.

The network interface 470 may be configured to facilitate a data flowbetween the processing device 400 and one or more other devices on adata network, including but not limited to data to/from other processingdevices, playback devices, storage devices, and the like. As such, theprocessing device 400 may be configured to transmit and receive audiocontent over the data network from one or more other devices incommunication with the processing device 400, network devices within alocal area network (LAN), or audio content sources over a wide areanetwork (WAN) such as the Internet. The processing device 400 may alsobe configured to transmit and receive sensor input over the data networkfrom one or more other devices in communication with the processingdevice 400, network devices within a LAN or over a WAN such as theInternet.

As shown in FIG. 4, the network interface 470 may include wirelessinterface(s) 472 and wired interface(s) 474. The wireless interface(s)472 may provide network interface functions for the processing device400 to wirelessly communicate with other devices in accordance with acommunication protocol (e.g., any wireless standard including IEEE802.11a/b/g/n/ac, 802.15, 4% mobile communication standard, and so on).The wired interface(s) 474 may provide network interface functions forthe processing device 400 to communicate over a wired connection withother devices in accordance with a communication protocol (e.g., IEEE802.3). While the network interface 470 shown in FIG. 4 includes bothwireless interface(s) 472 and wired interface(s) 474, the networkinterface 470 may in some embodiments include only wireless interface(s)or only wired interface(s).

The processing device may include one or more sensor(s) 440. The sensors440 may include, for example, inertial sensors (e.g., accelerometer,gyroscope, and magnetometer), a microphone, a camera, or a physiologicalsensor such as, for example, a sensor that measures heart rate, bloodpressure, body temperature, EEG, MEG, Near infrared (fNIRS), or bodilyfluid. In some example embodiments, the sensor may correspond to ameasure of user activity on a device such as, for example, a smartphone, computer, tablet, or the like.

The user interface 460 and display 480 can be configured to facilitateuser access and control of the processing device. Example user interface460 includes a keyboard, touchscreen on a display, navigation device(e.g., mouse), microphone, etc. Through the user interface 460, the usermay provide instructions to extend an audio track to a desired length.

Aspects of the present disclosure may exist in part or wholly in,distributed across, or duplicated across one or more physical devices.FIG. 5 shows one such illustrative system 500 in which the aspects ofthe present disclosure may be practiced. The system 500 illustratesseveral devices (e.g., computing device 510, audio processing device520, file storage 530, playback device 550, 560, and playback devicegroup 570) interconnected via a data network 505. Although the devicesare shown individually, the devices may be combined into fewer devices,separated into additional devices, and/or removed based upon animplementation. The data network 505 may be a wired network, a wirelessnetwork, or a combination of both.

In some example embodiments, the system 500 can include an audioprocessing device 520 that can perform various functions, including butnot limited to audio processing. In an example embodiment, the system500 can include a computing device 510 that can perform variousfunctions, including but not limited to, aiding the processing by theaudio processing device 520. In an example embodiment, the computingdevices 510 can be implemented on a machine such as the previouslydescribed processing device 400.

In an example embodiment, the system 500 can include a storage 530 thatis connected to various components of the system 500 via a network 505.The connection can also be wired (not shown). The storage 530 can beconfigured to store data/information generated or utilized by thepresently described techniques. For example, the storage 530 may storean audio track prior to the execution of the method 100 and an extendedaudio track generated by the method 100.

In an example embodiment, the system 500 can include one or moreplayback devices 550, 560 or a group of playback devices 570 (e.g.playback devices, speakers, mobile devices, etc.). In some exampleembodiments, a playback device may include some or all of thefunctionality of the computing device 510, the audio processing device520, and/or the file storage 530. As described previously, a sensor canbe based on the audio processing device 520 or it can be an externalsensor device 580 and data from the sensor can be transferred to theaudio processing device 520.

Additional examples of the presently described method and deviceembodiments are suggested according to the structures and techniquesdescribed herein. Other non-limiting examples may be configured tooperate separately or can be combined in any permutation or combinationwith any one or more of the other examples provided above or throughoutthe present disclosure.

It will be appreciated by those skilled in the art that the presentdisclosure can be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentlydisclosed embodiments are therefore considered in all respects to beillustrative and not restricted. The scope of the disclosure isindicated by the appended claims rather than the foregoing descriptionand all changes that come within the meaning and range and equivalencethereof are intended to be embraced therein.

It should be noted that the terms “including” and “comprising” should beinterpreted as meaning “including, but not limited to”. If not alreadyset forth explicitly in the claims, the term “a” should be interpretedas “at least one” and “the”, “said”, etc. should be interpreted as “theat least one”, “said at least one”, etc. Furthermore, it is theApplicant's intent that only claims that include the express language“means for” or “step for” be interpreted under 35 U.S.C. 112(f). Claimsthat do not expressly include the phrase “means for” or “step for” arenot to be interpreted under 35 U.S.C. 112(f).

The invention claimed is:
 1. A computer-implemented method comprising:extracting multi-dimensional features from an audio signal; segmentingthe audio signal into a first plurality of segments each having asegment size and extracted multi-dimensional features; segmenting theaudio signal into a second plurality of segments each having the segmentsize and the extracted multi-dimensional features; selecting at leastone segment from the first plurality of segments, and for each selectedsegment: comparing the multi-dimensional features of the segment withthe multi-dimensional features of the second plurality of segments;generating a self-similarity matrix having values indicating comparisonsof the multi-dimensional features of the selected segment withmulti-dimensional features of the second plurality of segments;selecting a first segment from the first plurality of segments and asecond segment from the second plurality of segments, wherein the firstand second segments correspond to a value in the self-similarity matrixthat is greater than a threshold; and joining a first portion of theaudio signal and a second portion of the audio signal, wherein the firstportion of the audio signal includes the first segment, and wherein thesecond portion of the audio signal includes the second segment.
 2. Themethod of claim 1, wherein extracting the multi-dimensional featuresfrom the audio signal further comprises: extracting, from the audiosignal, at least one of a spectrogram, a cochleagram, an amplitudemodulation spectrum, or mel-frequency cepstral coefficients.
 3. Themethod of claim 1, wherein the multi-dimensional features comprise atleast a spectral distribution on one axis and time on another axis. 4.The method of claim 1, wherein the time difference between the start ofadjacent segments is smaller than the segment size.
 5. The method ofclaim 1, wherein the joining of the first portion of the audio signaland the second portion of the audio signal includes overlapping aportion of the first segment and a portion of the second segment.
 6. Themethod of claim 5, wherein the overlapping corresponds to crossfading anending portion of the first segment with a beginning portion of thesecond segment.
 7. The method of claim 1, wherein the first portion ofthe audio signal includes audio prior to the first segment and whereinthe second portion of the audio signal includes audio after the secondsegment.
 8. The method of claim 1, wherein the first portion of theaudio signal further includes audio prior to the second segment andwherein the second portion of the audio signal includes audio after thefirst segment.
 9. A system comprising: a processor; and a tangible,non-transitory computer readable medium storing computer programinstructions, that when executed by the processor, cause the system toperform operations comprising: extracting multi-dimensional featuresfrom an audio signal; segmenting the audio signal into a first pluralityof segments each having a segment size and extracted multi-dimensionalfeatures; segmenting the audio signal into a second plurality ofsegments each having the segment size and the extractedmulti-dimensional features; selecting at least one segment from thefirst plurality of segments and for each selected segment: comparing themulti-dimensional features of the segment with the multi-dimensionalfeatures of the plurality of segments; generating a self-similaritymatrix having values indicating comparisons of the multi-dimensionalfeatures of the selected segment with multi-dimensional features of thesecond plurality of segments; selecting a first segment from the firstplurality of segments and a second segment from the second plurality ofsegments, wherein the first and second segments correspond to a value inthe self-similarity matrix that is greater than a threshold; and joininga first portion of the audio signal and a second portion of the audiosignal, wherein the first portion of the audio signal includes the firstsegment, and wherein the second portion of the audio signal includes thesecond segment.
 10. The system of claim 9, wherein the operationsfurther comprise: extracting, from the audio signal, at least one of aspectrogram, a cochleagram, an amplitude modulation spectrum, ormel-frequency cepstral coefficients.
 11. The system of claim 9, whereinthe multi-dimensional features comprise at least a spectral distributionon one axis and time on another axis.
 12. The system of claim 9, whereinthe time difference between the start of adjacent segments is smallerthan the segment size.
 13. The system of claim 9, wherein the joining ofthe first portion of the audio signal and the second portion of theaudio signal includes overlapping a portion of the first segment and aportion of the second segment.
 14. The system of claim 13, wherein theoverlapping corresponds to crossfading an ending portion of the firstsegment with a beginning portion of the second segment.
 15. The systemof claim 9, wherein the first portion of the audio signal includes audioprior to the first segment and wherein the second portion of the audiosignal includes audio after the second segment.
 16. The system of claim9, wherein the first portion of the audio signal includes audio prior tothe second segment and wherein the second portion of the audio signalincludes audio after the first segment.
 17. The system of claim 9,wherein the operations further comprise: generating an extended audiofile, including the joined first and second portions, dynamically duringa playback of the audio file.
 18. The system of claim 9, wherein theoperations further comprise: generating an extended audio file,including the joined first and second portions, based on a user-providedplayback length.
 19. A tangible, non-transitory computer readable mediumstoring computer program instructions, that when executed by aprocessor, cause operations comprising: extracting multi-dimensionalfeatures from an audio signal; segmenting the audio signal into a firstplurality of segments each having a segment size and extractedmulti-dimensional features; segmenting the audio signal into a secondplurality of segments each having the segment size and the extractedmulti-dimensional features; selecting at least one segment from thefirst plurality of segments and for each selected segment: comparing themulti-dimensional features of the segment with the multi-dimensionalfeatures of the plurality of segments; generating a self-similaritymatrix having values indicating comparisons of the multi-dimensionalfeatures of the selected segment with multi-dimensional features of thesecond plurality of segments; selecting a first segment from the firstplurality of segments and a second segment from the second plurality ofsegments, wherein the first and second segments correspond to a value inthe self-similarity matrix that is greater than a threshold; and joininga first portion of the audio signal and a second portion of the audiosignal, wherein the first portion of the audio signal includes the firstsegment, and wherein the second portion of the audio signal includes thesecond segment.
 20. The tangible, non-transitory computer readablemedium of claim 19, wherein the operations further comprise: extracting,from the audio signal, at least one of a spectrogram, a cochleagram, anamplitude modulation spectrum, or mel-frequency cepstral coefficients.